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Journal of Animal Science - Animal Genetics

Population genetic structure in farm and feral American mink (Neovison vison) inferred from RAD sequencing-generated single nucleotide polymorphisms1


This article in JAS

  1. Vol. 93 No. 8, p. 3773-3782
    Received: Feb 09, 2015
    Accepted: May 28, 2015
    Published: July 24, 2015

    3 Corresponding author(s):

  1. J. P. Thirstrup 3*2,
  2. A. Ruiz-Gonzalez†‡§22,
  3. J. M. Pujolar#22,
  4. P. F. Larsen,
  5. J. Jensen*,
  6. E. Randi†¶,
  7. A. Zalewski** and
  8. C. Pertoldi††
  1. * Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark
     Conservation Genetics Laboratory, National Institute for Environmental Protection and Research (ISPRA), Via Cà Fornacetta 9, I-40064 Ozzano dell’Emilia (BO), Italy
     Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU), Avda. Miguel de Unamuno, 3, E-01006 Vitoria-Gasteiz, Spain
    § Department of Zoology and Animal Cell Biology, University of the Basque Country (UPV/EHU), C/Paseo de la Universidad 7, E-01006 Vitoria-Gasteiz, Spain
    # Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark
     Kopenhagen Research, Agro Food Park15, DK-8200 Aarhus N, Denmark
     Department 18/Section of Environmental Engineering, Aalborg University, Aalborg, Denmark
    * *Mammal Research Institute, Polish Academy of Sciences, 17-230 Białowieża, Poland
     †Aalborg Zoo, Aalborg, Denmark


Feral American mink populations (Neovison vison), derived from mink farms, are widespread in Europe. In this study we investigated genetic diversity and genetic differentiation between feral and farm mink using a panel of genetic markers (194 SNP) generated from RAD sequencing data. Sampling included a total of 211 individuals from 14 populations, 4 feral and 10 from farms, the latter including a total of 7 color types (Brown, Black, Mahogany, Sapphire, White, Pearl, and Silver). Our study revealed similar low levels of genetic diversity in both farm and feral mink. Results are consistent with small effective population size as a consequence of line selection in the farms and founder effects of a few escapees from the farms in feral populations. Moderately high genetic differentiation was found between farm and feral animals, suggesting a scenario in which wild populations were founded from farm escapes a few decades ago. Currently, escapes and gene flow are probably limited. Genetic differentiation was higher among farm color types than among farms, consistent with line selection using few individuals to create the lines. Finally, no indications of inbreeding were found in either farm or feral samples, with significant negative FIS values found in most farm samples, showing farms are successful in avoiding inbreeding.


The American mink (Neovison vison) is bred for its fur and is of high economic importance. Originally endemic to North America, the species was introduced in Europe and Asia in the 1950s, following the commercialization of fur production (Bonesi and Palazon, 2007). Selection of color mutants has produced many different color types (White, Pearl, Silver, etc.) relative to the original wild type (i.e., Brown; Trapezov, 1997). As a consequence of many generations of artificial selection for economically important fur traits, farmed mink show larger body size, shorter and thinner guard hair, and denser underfur than the undomesticated wild mink (Lagerkvist, 1997; Belliveau et al., 1999). In addition, domesticated mink are less aggressive toward humans (Malmkvist and Hansen, 2002). Even though domesticated farm mink would seem a priori unfit to survive in the wild, several feral populations have been established in Europe since they were introduced in mink farms (Bonesi and Palazon, 2007). Feral mink populations in Europe originate from accidental escapes from farms during animal handling or from deliberate releases either as game animals for hunting or by activists protesting against fur farming (Pertoldi et al., 2013). Escaped mink have adapted to the wild even if some color types might be unsuitable for camouflage and are detrimental for survival skills such as hunting, self-defense, and mate seeking (Kidd et al., 2009).

The aim of the current study was to compare levels of genetic diversity in farmed and feral American mink from Danish and Polish populations and infer population structure and gene flow among populations. We also investigated genetic differentiation among different color types in farm mink, including Brown, Black, Mahogany, White, Pearl, Silver, and Sapphire. We were particularly interested in inferring inbreeding in both farm and feral populations since inbreeding has been shown to have a negative impact on fertility in American mink (Demontis et al., 2011).


Samples used for this study were obtained at pelting or from hunters following Danish and Polish legality for production animals and wildlife management. Therefore, Animal Care and Use Committee approval was not obtained for this study.


A total of 211 American mink from 14 sampling locations were analyzed (Fig. 1, Table 1), including 4 feral samples (N = 98) and 10 farm samples (N = 113). All feral samples were collected in Poland, 2 from northwestern Poland (samples 1 and 2) and 2 from northeastern Poland (samples 3 and 4). All feral samples represent the wild Brown color type. Farm samples were collected from Poland (sample 5) and Denmark (samples 6 to 14). Farm samples included a total of 7 different color types (Brown, Black, White, Mahogany, Pearl, Silver, and Sapphire). Two of the feral populations had previously been described by Zalewski et al. (2010, 2011), although the individuals that were sampled are from different years (2003 to 2007 in Zalewski et al. [2010, 2011] and 2009 to 2011 in this study), whereas the Danish samples were described by Demontis et al. (2011). Blood was sampled from the Danish farm individuals from unrelated females at pelting, whereas muscle tissue was sampled from the Polish individuals. Feral samples were obtained either from live-trapped individuals or from animals killed by hunters.

Figure 1.
Figure 1.

Map showing population location.


View Full Table | Close Full ViewTable 1.

Summary of populations and genetic diversity indices, including sample abbreviation; country of origin; type; number of animals in each sample; mean number of alleles (MNA); effective number of alleles (ENA); information index (I); observed (HO), expected (HE), and unbiased expected (uHE) heterozygosity; and inbreeding coefficient (FIS) with SE1

Population Sample Country2 Type No. of animals MNA ENA I HO HE uHE FIS
1 Drawa PL (west) Feral 19 1.81 (0.03) 1.47 (0.03) 0.41 (0.02) 0.27 (0.02) 0.27 (0.01) 0.28 (0.01) 0.003 (0.02)
2 Warta3 PL4 (West) Feral 51 1.90 (0.02) 1.51 (0.03) 0.45 (0.02) 0.30 (0.01) 0.30 (0.01) 0.30 (0.01) 0.005 (0.01)
3 Biebrza PL4,5 (east) Feral 16 1.87 (0.02) 1.50 (0.03) 0.44 (0.12) 0.30 (0.02) 0.30 (0.01) 0.30 (0.01) −0.01 (0.02)
4 Narew PL (east) Feral 12 1.83 (0.03) 1.48 (0.03) 0.42 (0.02) 0.29 (0.02) 0.28 (0.01) 0.30 (0.01) −0.02 (0.02)
5 KA (Brown) PL Farm 36 1.96 (0.01) 1.54 (0.02) 0.48 (0.01) 0.33 (0.01) 0.32 (0.01) 0.32 (0.01) −0.04 (0.01)*
6 SVS (Brown) DK6 Farm 11 1.90 (0.02) 1.52 (0.02) 0.46 (0.02) 0.32 (0.02) 0.31 (0.01) 0.32 (0.01) −0.05 (0.03)
7 ST (Brown) DK6 Farm 12 1.86 (0.03) 1.50 (0.02) 0.45 (0.02) 0.34 (0.02) 0.30 (0.01) 0.31 (0.01) −0.12 (0.02)*
8 TP (Mahogany) DK6 Farm 10 1.89 (0.02) 1.53 (0.02) 0.47 (0.02) 0.34 (0.02) 0.31 (0.01) 0.33 (0.01) −0.08 (0.02)*
9 FF (Black) DK6 Farm 5 1.68 (0.03) 1.44 (0.03) 0.38 (0.02) 0.29 (0.02) 0.26 (0.01) 0.29 (0.02) −0.13 (0.03)*
10 JR (Black) DK6 Farm 8 1.80 (0.03) 1.49 (0.03) 0.43 (0.02) 0.33 (0.02) 0.29 (0.01) 0.31 (0.01) −0.13 (0.03)*
11 SVS (White) DK6 Farm 8 1.77 (0.03) 1.45 (0.03) 0.40 (0.02) 0.28 (0.02) 0.27 (0.01) 0.29 (0.01) −0.05 (0.03)
12 TP (Pearl) DK6 Farm 7 1.60 (0.04) 1.38 (0.03) 0.33 (0.02) 0.25 (0.02) 0.22 (0.02) 0.24 (0.02) −0.11 (0.03)*
13 FA (Silver) DK6 Farm 8 1.77 (0.03) 1.47 (0.03) 0.41 (0.02) 0.32 (0.02) 0.27 (0.01) 0.29 (0.02) −0.15 (0.02)*
14 JC (Sapphire) DK6 Farm 8 1.66 (0.03) 1.40 (0.03) 0.35 (0.02) 0.27 (0.02) 0.24 (0.01) 0.25 (0.02) −0.14 (0.02)*
1Standard deviation is given in parentheses. Significance of FIS is inferred using the Wald (1943) test and is indicated by asterisk (*).
2DK: Denmark, PL: Poland.
3Warta individuals were collected in 2 different locations: site 1: n = 19; site 2 = 32.
4Previously described in Zalewski et al. (2010, 2011), but analyzed samples were collected in different years.
5Previously described in Michalska-Parda et al. (2009), but analyzed samples were collected in different years.
6Previously described in Demontis et al. (2011).

DNA Extraction and SNP Genotyping

DNA was isolated from tissue and blood samples using a Qiagen DNeasy Blood and Tissue kit (Qiagen Inc., Valencia, CA) according to the manufacturer’s instructions and from blood capillaries using a NucleoMag Blood kit (Macherey Nagel, Düren, Germany) on a Tecan Freedom Evo 150 robot (Tecan, Männedorf, Switzerland).

All animals were genotyped at a panel of 380 SNP (Thirstrup et al., 2014b) using a NGS Ion Torrent sequencing platform. All SNP were typed using the GenoSkan Genotyping approach, which resembles the principle of the ligase-mediated gene detection technique described by Landegren et al. (1988) using modified protocol and primers (see Thirstrup et al., 2014b for further details).

Call rates for each SNP in the 380-SNP panel were calculated as 1) the frequency of SNP that was called for each mink and 2) the frequency of mink that was genotyped for each SNP. Single nucleotide polymorphisms with a call rate less than 80%, SNP for which all genotyped individuals were heterozygotes, and SNP with minor allele frequencies (MAF) less than 1% were excluded.

Finally, we tested the neutrality of the markers to remove SNP putatively under selection from the panel. We used the FST outlier detection approach (Beaumont and Nichols, 1996) implemented in LOSITAN (Antao et al., 2008). LOSITAN uses a coalescent-based simulation approach to identify outliers on the basis of the distributions of heterozygosity and FST. LOSITAN was run under the infinite alleles model using 500,000 simulations, a “neutral” mean FST (potentially nonneutral loci are removed before calculating the initial mean FST), confidence intervals of 95%, and a false discovery rate of 5%. Outlier tests were conducted on the filtered SNP panel (i.e., after excluding SNP putatively subject to genotyping error as described in the previous paragraph).

Data Analysis

Within sample, genetic diversity was assessed by observed (HO), expected (HE), and unbiased expected (uHE) heterozygosity as well as mean number of alleles (MNA) and effective number of alleles (ENA), information index (I), and inbreeding coefficient (FIS) using GenAlEx 6.5 (Peakall and Smouse, 2012). Diversity values across samples were compared statistically by one-way ANOVA and a Tukey’s test for pairwise comparisons using the R Stats Package (R Core Team, Vienna, Austria).

Deviations from Hardy-Weinberg equilibrium (HWE), linkage disequilibrium, differences in allele frequencies and overall genetic differentiation (FST), and pairwise FST estimates between all pairs of samples were calculated using GENEPOP (Raymond and Rousset, 1995). The significance of FST and FIS was inferred using the Wald test. Significance levels for multiple comparisons were adjusted using the sequential Bonferroni technique at the 0.05 level (Rice, 1989). Furthermore, nonhierarchical and hierarchical analyses of molecular variance (AMOVA) were conducted in ARLEQUIN (Excoffier and Lischer, 2010). In the first analysis, we partitioned molecular variance among regions (Denmark vs. Poland) and among samples within regions. We also partitioned molecular variance according to type (farm vs. feral) and according to color. Pairwise FST values between populations and interindividual genetic distances were used to perform a principal component analysis (PCA) in GenAlEx to visualize the genetic relationship at the population and at the individual level, respectively. Additionally, a multivariate ordination by multidimensional scaling (MDS) analysis was also conducted considering only the different color types using the R package.

Population structure was further investigated using the Bayesian model-based clustering algorithm implemented in STRUCTURE 2.3.4 (Pritchard et al., 2000), which infers the most likely number of groups in the data. The software organizes individuals into clusters (K) with a given likelihood, which may represent putative populations. Ten independent runs were conducted for each value of K (K = 1 to 14) using 500,000 Monte Carlo Marcov Chain (MCMC) iterations, after a burn-in of 50,000 iterations. We did not introduce prior knowledge about the population of origin, and we assumed correlated allele frequencies and the admixture model (Falush et al., 2003). To estimate the uppermost optimal number of clusters in the data sets, 1) Evanno’s delta K method and 2) log probabilities for each K value (L(K)) were estimated. The estimated cluster membership coefficients of the 10 runs for the optimal value of K were permuted so that all replicates have the closest match possible and were then averaged across replicates using the Greedy algorithm of the CLUMMP software (Jakobsson and Rosenberg, 2007).


SNP Genotyping and Filtering

The overall call rate for all 380 markers and all the individuals was 80.08%. Call rates for individuals were in the range of 60.53% to 96.84%, and call rates for markers were in the range of 35% to 100%. After filtering SNP with call rates <80% and MAF <1%, 215 markers were left for further analyses. The overall call rate was then 92%. Call rates for markers were in the range of 80.28% to 100%, and call rates for individuals were in the range of 83.16% to 100%.

A neutrality test using LOSITAN identified a total of 21 outlier SNP, of which 14 were under balancing selection and 7 were under directional selection. All outliers were excluded from further analyses. In the end, after excluding 165 SNP because of low call rates and 21 SNP because of nonneutrality, 194 SNP were considered suitable for population genetic analyses and genotyped for all 14 mink samples in our study.

Genetic Variability

A summary of genetic diversity at 194 SNP for all populations is shown in Table 1. Measures of genetic variability were very similar across populations (i.e., HO ranged from 0.25 to 0.34; HE ranged from 0.22 to 0.31). No significant differences were found when comparing pooled Poland (HO = 0.30; HE = 0.32; MNA = 1.99) and pooled Denmark (HO = 0.31; HE = 0.32; MNA = 2.00) samples. Similarly, no significant differences were found when comparing genetic variability in pooled feral (HO = 0.29; HE = 0.31; MNA = 1.97) and pooled farm (HO = 0.32; HE = 0.32; MNA = 2.00) samples. When comparing color types, the highest genetic variability was found in Brown (HO = 0.33; HE = 0.32; MNA = 1.99), Mahogany (HO = 0.34; HE = 0.31; MNA = 1.89), and Black (HO = 0.32; HE = 0.30; MNA = 1.89), whereas the lowest genetic variability was observed in Pearl (HO = 0.25; HE = 0.22; MNA = 1.60) and Sapphire (HO = 0.27; HE = 0.24; MNA = 1.65). A one-way ANOVA testing for differences in HE among color types was highly significant (F13,2702 = 4.81). The Tukey test for pairwise comparisons showed significantly lower HE in Pearl and Sapphire than in all the other color types.

All feral samples showed FIS values close to zero and in HWE. By contrast, all farm samples showed negative FIS values, including 8 samples for which negative FIS values were statistically significant. The FIS values for all samples are summarized in Table 1.

Genetic Differentiation

When comparing allele frequencies between all samples at all loci, significant differences were found at most loci (172 out of 194 loci). After Bonferroni correction for multiple testing, 91 loci showed statistically significant differences. When comparing pooled Polish and pooled Danish samples, significant differences were found at 16 loci (after Bonferroni correction). When comparing pooled farm and pooled feral samples, significant differences were found at 31 loci after Bonferroni correction. Similarly, 39 loci showed significant differences (after Bonferroni correction) when comparing pooled east and pooled west Poland samples. Finally, 24 loci showed significant differences (after Bonferroni correction) when comparing color types.

A highly significant overall FST = 0.08 was found across samples (P < 0.001). Highly significant paired FST values were found between most samples. Only 8 comparisons out of 91 were not statistically significant. Pairwise FST estimates between all sample pairs are shown in Table S1 in the supplementary material.

An AMOVA partitioning genetic diversity according to country of origin (Denmark vs. Poland; FST = 0.087; P < 0.001) found higher differences within countries (FSC = 0.076; P < 0.001) than between countries ( FCT = 0.011; P < 0.001; Table 2). When comparing feral and farm individuals (FST = 0.084; P < 0.001), the analysis suggested a higher differentiation within groups (FSC = 0.080; P < 0.001) than between groups (FCT = 0.004; P < 0.001). Finally, when comparing color types (considering only the 10 farm samples), genetic differentiation (FST = 0.076; P < 0.001) showed a two-fold higher differentiation among color types (FCT = 0.052; P < 0.001) than within color types (FSC = 0.025; P < 0.001).

View Full Table | Close Full ViewTable 2.

Hierarchical molecular variance analysis using ARLEQUIN considering the following comparisons: 1) country of origin (Poland vs. Denmark), 2) type (feral vs. farm), and 3) color types

Comparison FST (overall differentiation) FSC (within groups) FCT (among groups)
Poland vs. Denmark 0.087* 0.076* 0.011*
Feral vs. farm 0.084* 0.080* 0.004*
Color types 0.076* 0.025* 0.052*
*P < 0.05.

Visualization of the genetic relationship among populations and individuals using PCA is shown in Fig. 2a and 2b. The first 3 coordinates always explained a large portion of the genetic variation: 53.90% at the population level and 61.88% at the individual level. At the population level (Fig. 2a), the first coordinate clearly separates feral populations from farm populations. The PCA also showed how the White Danish farm population appeared to be more closely related to the feral Polish populations. Similarly, at the individual level (Fig. 2b), the first coordinate of the PCA plot for the 211 mink individuals clearly distinguishes between farm and feral mink, with the exception of 4 wild animals that clustered together with the farm stock. At the population level, the second coordinate separates the Pearl, Silver, and White color types, which appear to be the most differentiated color types. In all PCA, Brown and Black color types cluster together. A MDS analysis considering only farm samples and pooling samples from the same color showed a similar pattern (Fig. 3). Sapphire, Pearl, and White appeared to be the most differentiated color types, clearly separated from the rest. Brown, Black, and Mahogany types clustered together, with Silver being slightly separated from them.

Figure 2.
Figure 2.

Principal coordinates analysis. Plots of the values of the first, second, and third principal coordinates among all (a) samples and (b) individuals. Solid circles: feral populations; open circles: farm populations.

Figure 3.
Figure 3.

Multivariate ordination by multidimensional scaling (MDS). Plot of first and second ordinates considering only pooled color types from farm samples.


Finally, we analyzed population substructure and estimated the number of clusters or groups in our sampling using STRUCTURE. Using Evano’s criterion, a scenario with 2 groups was suggested (K = 2; Fig. S1), with 1 cluster including the 2 western Poland samples (Drawa and Warta site 2) and a second cluster including the rest of samples. However, using L(K) as criterion, a scenario with 11 groups was suggested (K = 11; Fig. S1). The inferred population membership coefficients are shown in Table 3 and in Fig. 4. Feral samples are clearly subdivided into 3 genetic clusters: the 2 western Poland samples appeared as 2 separate clusters (i.e., Drawa and Warta site 2 in one and Warta site 1 in another), whereas the 2 eastern Polish samples formed a single genetic cluster (i.e., Biebrza and Narew; Fig. 4). On the other hand, farm samples showed a clear pattern of genetic subdivision, and all color types appeared as separate clusters with the exception of Mahogany, which appeared to be admixed between Black and Brown. White, Pearl, Silver, and Sapphire were clearly identified as different clusters with high probabilities of population membership (i.e., Q > 0.8). The situation was more complex for Brown and Black: all 3 Brown samples were grouped together in the same cluster, whereas the 2 Black samples appeared as 2 separate clusters. However, although Brown and Black individuals were assigned to their own separate clusters, they all showed some admixture among them.

View Full Table | Close Full ViewTable 3.

Proportion of membership to the 11 clusters inferred by STRUCTURE1

Population Sample I II III IV V VI VII VIII IX X XI
1 Drawa 0.018 0.040 0.015 0.027 0.016 0.745 0.014 0.017 0.026 0.013 0.069
2 Warty 0.009 0.026 0.026 0.019 0.017 0.223 0.620 0.014 0.012 0.012 0.022
3 Biebrza 0.013 0.009 0.888 0.007 0.015 0.006 0.008 0.016 0.018 0.008 0.012
4 Narew 0.013 0.018 0.865 0.018 0.019 0.009 0.015 0.013 0.006 0.008 0.016
5 KA (Brown) 0.084 0.053 0.04 0.016 0.016 0.008 0.012 0.011 0.125 0.364 0.272
6 SVS (Brown) 0.018 0.025 0.02 0.007 0.018 0.009 0.009 0.013 0.065 0.556 0.259
7 ST (Brown) 0.011 0.012 0.007 0.008 0.015 0.006 0.006 0.011 0.012 0.870 0.042
8 TP (Mahogany) 0.019 0.122 0.008 0.012 0.009 0.012 0.016 0.02 0.404 0.116 0.261
9 FF (Black) 0.006 0.132 0.01 0.006 0.014 0.014 0.022 0.072 0.028 0.204 0.490
10 JR (Black) 0.006 0.009 0.007 0.009 0.007 0.008 0.027 0.009 0.899 0.007 0.013
11 SVS (White) 0.008 0.047 0.01 0.079 0.790 0.005 0.008 0.007 0.011 0.018 0.018
12 TP (Pearl) 0.006 0.009 0.006 0.943 0.004 0.003 0.003 0.011 0.006 0.005 0.005
13 FA (Silver) 0.007 0.798 0.009 0.017 0.013 0.007 0.007 0.094 0.009 0.011 0.026
14 JC (Sapphire) 0.005 0.028 0.005 0.01 0.004 0.007 0.004 0.914 0.009 0.006 0.009
1The most likely clusters are in bold.
Figure 4.
Figure 4.

Admixture analysis using STRUCTURE, in which individuals were assigned according to the most likely K (i.e., K = 11). Each vertical line represents 1 individual. Populations are numbered as in Table 1.



Moderately High Genetic Differentiation Between Farm and Feral Mink

Our study, based on a large panel of 194 RAD sequencing-derived SNP, showed significant genetic differentiation between farm and feral mink populations, clearly supported by FST, PCA, and STRUCTURE analyses. A comparison of allelic frequencies showed significant differences at 91 out of the 194 SNP analyzed, which represents 47% of the loci analyzed. Overall genetic differentiation was moderately high (FST = 0.084), and all 40 pairwise comparisons between farm and feral samples were statistically significant. The amount of genetic differentiation found is concordant with the previous study of Michalska-Parda et al. (2009) using 11 microsatellite loci. A comparison of 5 feral and 10 farm populations from Poland, the latter representing 4 different color types, showed significant FST values in the range of 0.036 to 0.084. Similarly, Kidd et al. (2009) found significant differences between farm (3 color types) and feral mink populations in North America using 10 microsatellite markers, with pairwise FST values ranging from 0.05 to 0.15 and an average FST of 0.06.

The genetic differentiation and population substructuring found suggest that escapes are not recent and potentially occurred a long time ago because no differentiation would be expected if the escapes were recent with no time to diverge genetically. Although gene flow from the wild to the farm is not expected (feral animals have little chance to return to the farms), gene flow from the farms to the wild would a priori be expected. However, the high genetic differentiation found between farm and feral mink suggests a low exchange of individuals and low levels of gene flow. Indeed, an individual PCA analysis showed only 4 out of 98 feral individuals clustering together with the farm individuals, representing possible recent escapes.

In recent years, safety measures in mink farms have been increased relative to a few decades ago. In this sense, fences have been built around many mink farms following the concern of farmers about transmission of viruses and diseases from the wild. Building fences not only protects mink against diseases but also prevents escapes, so if an animal escapes during handling, it can be caught and caged again. Moreover, farms are also more secure from activists, so that they cannot access the farms as easily as in the past. Overall, it seems that the number of mink escapes at present is relatively small, which would explain the low levels of gene flow suggested in our study between farm and wild mink.

No Differences in Genetic Variability between Farm and Feral Mink

Our study shows similar values of genetic diversity in farm and feral mink populations, as no significant differences were found in terms of heterozygosity or number of alleles. The result is surprising since feral populations of American mink in Europe are all derived from farm populations (Bonesi and Palazon 2007), and thus, we would expect lower genetic variation in feral populations because of the founder effect.

American mink originated in North American and Canada. The first mink were imported to Fennoscandia and the former Soviet Union for fur farming in the early 1920s and were introduced in Europe when fur farms were established in the 1950s. Soon after the establishment of fur farms, the first escapes were observed. The earliest record of free-living mink is from 1928 in Sweden, from 1930 in Norway, and from 1932 in Finland (Kauhala 1996). At present, feral populations of mink are found throughout Europe, particularly in northeastern Europe (Bonesi and Palazon, 2007; Michalska-Parda et al., 2009; Zalewski et al., 2010, 2011). All feral populations are either accidental escapes from farms during handling or premeditated releases of farm animals either for hunting or by activists protesting against mink farming. In addition to being founded by relatively few individuals, feral populations face a further disadvantage: since escapers have been bred and raised in captivity, they are used to being fed and being kept in a protected environment, whereas in nature they have to hunt and provide food for themselves and protect themselves against predators and other dangers (Kidd et al., 2009). Furthermore, mutations in color types artificially selected in farms might be disadvantageous in nature (i.e., White) because they render the animal more conspicuous and easier to spot by hunters or predators. Color types are Mendelian inherited, and the Brown color type is dominant over mutant color types (Ness et al., 1988). This means that escaped mink harboring mutant color types need to survive only long enough to breed with a Brown (i.e., wild type) in 1 generation to produce offspring with a more favorable color type for survival in nature.

We hypothesize that the similar values of genetic diversity found could be a consequence of both farm and feral populations originating from few individuals: low genetic variability in farm populations might be due to assortative mating, whereas feral mink might present low genetic variation because of founder effects and reduced gene flow between populations. In the case of farm populations, lines of specific color types at a given farm are artificially selected (assortative mating) to improve fur quality and body size. Color lines are started with very few individuals, sometimes as few as 2 that were kept in the line and selected on the basis of continuous backcrossing to avoid inbreeding; hence, the low variability found in color lines can be attributed to a founder effect. Similarly, feral populations originate from farm escapes, thus again constituted by few individuals. Moreover, female mink are territorial (Zalewski et al., 2011) and breed with related males from neighboring areas. Hence, the lack of gene flow between feral mink populations could also explain why genetic variation is low, as mixing between populations does not seem to occur.

Our results are concordant with the study of Michalska-Parda et al. (2009) using 11 microsatellites. The analysis of 182 farm individuals from 10 mink farms and 87 feral individuals from 5 areas in Poland showed no differences in values of observed and expected heterozygosity. Similarly, Zalewski et al. (2011), using mitochondrial DNA, reported no differences in haplotype or nucleotide diversity when comparing 166 farm and 276 feral mink from Poland.

No Evidence for Inbreeding

Genetic data in our study also suggest that the populations of mink studied are not being inbred. When populations are inbred, positive FIS values are expected since the offspring of closely related individuals are expected to share similar genes and likely to be homozygous over a few generations. Surprisingly, our study showed FIS values close to zero for feral populations and significant negative FIS values in the case of farm populations. Negative FIS values reveal an increase in heterozygosity, usually due to outbreeding, in which mating is more random and genes are more likely to be different.

Our results contrast with the study of Michalska-Parda et al. (2009) performed on Polish mink, which found high levels of homozygosity, suggestive of inbreeding, in both feral and farm populations. Similarly, a study performed on farm mink from Nova Scotia in Canada (Belliveau et al., 1999) showed a deficit of heterozygotes concordant with inbreeding. However, in the case of the study of Michalska-Parda et al. (2009), genetic analysis was conducted on a limited number of markers (11 microsatellites), whereas our study genotyped individuals at a much larger panel of 194 SNP, so the differences between studies might be due to the different nature and number of markers used.

Our results showing negative FIS values for farm populations make sense if we take into consideration that avoidance of inbreeding has been of much concern in breeding programs for farm populations because of the deleterious effect on litter size (Hansen et al., 2010). Demontis et al. (2011) showed a linear correlation between inbreeding level and fecundity, so inbreeding decreases fecundity in farmed mink. In this sense, Danish mink breeders often exchange individuals with each other, and crossbreeding is common practice in Denmark to avoid inbreeding (Thirstrup et al., 2014a).Our results suggest that initiatives taken by breeders are effective when it comes to preventing inbreeding in the populations studied from Denmark and Poland.

Genetic Differentiation among Feral Mink Populations in Poland

Our study suggests significant genetic differences among feral mink populations in Poland. Overall, STRUCTURE analysis, also supported by FST values among populations, revealed that feral Polish populations are clearly subdivided into 3 genetic clusters, corroborating the existence of an east–west pattern of geographical population structure. The 2 populations from western Poland appeared as 2 separate clusters (i.e., Drawa plus Warta site 2 and Warta site 1), whereas the 2 from eastern Poland formed a single genetic cluster (i.e., Biebrza and Narew). This result in accordance with the study of Zalewski et al. (2011), who also found significant differentiation between east and west Polish feral populations using mitochondrial DNA. Our results suggest ongoing gene flow between the 2 east Poland locations, which is plausible, as Biebrza and Narew are situated on the same riverbanks in eastern Poland, with no apparent barriers to the movement and exchange of individuals in the area. On the other hand, our study suggests no gene flow between the east and west Poland populations. This makes sense since sampling locations were separated by a geographic distance of over 500 km, a distance that seems difficult for mink to cover.

The most likely explanation for the differentiation between eastern and western Polish feral populations is a different population source. We hypothesize that mink from eastern Poland are most likely descendants from Belarus migrants. This hypothesis is in agreement with observational data for feral mink in Poland. Animals originally released in Belarus in the 1980s and 1990s were observed first in eastern Poland and later in the northern and northwestern regions (Brzeziński and Marzec, 2003), which points to a Belarus origin for the east Polish mink populations. The significant differences found between the east and west populations in our study suggest a different origin for the west Polish populations, which are probably descendants from farm escapes in the western region.

High Genetic Differentiation among Color Types

Our SNP survey shows high genetic differences among color types of farm mink. Particularly, partitioning of genetic diversity suggested more differences among color types than between farm and feral mink. This finding was also confirmed by the large FST values found among color types. Finally, STRUCTURE analysis suggested each color type is a different cluster with the exception of Mahogany, which appeared as an admixture between Black and Brown. The genetic differentiation between color types within each farm clearly indicates that mink from the same farm have been kept as separate (color) populations. Brown is the original color type, whereas other colors (i.e., Silver, Sapphire, White, and Silver) are mutations. These mutations are recessive to the Brown color type and can be expressed only in its homozygotic status. Color selection includes assortative mating and breeding of very few individuals to generate the desired color type and going through several generations of backcrossing and crossing of F2 to keep the relevant combinations. In this way, color types should be greatly affected by random genetic drift because of the small population sizes of the groups. Genetic drift could explain the large genetic difference found among color types, which are even larger than those found between farm and feral mink.

The differences found across color types are consistent with the production of color types in mink farms. In this sense, Mahogany is produced in the farms by crossing Black and Brown, which is consistent with the results from STRUCTURE. The STRUCTURE analysis also showed some admixture between Brown and Black, with the Black type also showing significant differences between farms. These results can be explained by how the Black type is produced at the farms since Black can be produced in different ways, either by breeding 2 pure Black parents or by strong selection for darkness in Brown individuals.

Finally, the finding of larger differences between color types than between farms suggests that the selective methods used to generate the different color types in the farms might be ubiquitous. This means that different farms in Poland or Denmark select for the same genetic gain, which would explain the lack of genetic differences among color types from different farms. Alternatively, there might have been an exchange of animals between mink farms across countries.


In this study we have efficiently genotyped 194 highly diagnostic SNP specific for population genetic analyses in farm and feral American mink using a novel next-generation sequencing approach. The SNP assay was efficiently used to infer genetic structure and variability of several mink populations in Denmark and Poland. Overall, our study shows a moderately high level of genetic differentiation between feral and farm populations, which suggests a scenario with higher rates of farm escapes in the past than at present. Differences between color types were higher than between farms, attributable to the low number of individuals used to generate the different color lines. Finally, no indications of inbreeding were found in either feral or farm populations. This result seems to suggest that measures taken by the farms to avoid inbreeding are effective, especially since inbreeding has been shown to exert a negative impact on fecundity in American mink.





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